Providing small-scale information about weather and climate is challenging, especially for variables strongly controlled by processes that are unresolved by low-resolution (LR) models. This paper explores emerging machine learning methods from the fields of image super-resolution (SR) and deep learning for statistical downscaling of near-surface winds to convection-permitting scales. Specifically, Generative Adversarial Networks (GANs) are conditioned on LR inputs from a global reanalysis to generate high-resolution (HR) surface winds that emulate those simulated over North America by the Weather Research and Forecasting (WRF) model. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves non-idealized LR inputs from a coarse-resolution reanalysis. In addition to matching the statistical properties of WRF simulations, GANs quickly generate HR fields with impressive realism. However, objectively assessing the realism of the SR models requires careful selection of evaluation metrics. In particular, performance measures based on spatial power spectra reveal the way that GAN configurations change spatial structures in the generated fields, where biases in spatial variability originate, and how models depend on different LR covariates. Inspired by recent computer vision research, a novel methodology that separates spatial frequencies in HR fields is used in an attempt to optimize the SR GANs further. This method, called frequency separation, resulted in deterioration in realism of the generated HR fields. However, frequency separation did show how spatial structures are influenced by the metrics used to optimize the SR models, which led to the development of a more effective partial frequency separation approach.

翻译：提供天气和气候的小尺度信息具有挑战性，尤其对于受低分辨率（LR）模型无法解析的过程强烈控制的变量。本文探讨了从图像超分辨率（SR）和深度学习领域兴起的最新机器学习方法，用于将近地表风统计降尺度至对流允许尺度。具体而言，生成对抗网络（GAN）以全球再分析的低分辨率输入为条件，生成高分辨率（HR）地表风，模拟美国天气研究与预报（WRF）模型在北美地区模拟的结果。与传统SR模型（其中LR输入是HR图像的理想化粗化版本）不同，WRF模拟涉及来自粗糙分辨率再分析的非理想化LR输入。除了匹配WRF模拟的统计特性外，GAN能够快速生成具有惊人真实感的HR场。然而，客观评估SR模型的真实感需要仔细选择评估指标。特别是，基于空间功率谱的性能度量揭示了GAN配置如何改变生成场中的空间结构、空间变异性偏差的来源以及模型对不同LR协变量的依赖程度。受近期计算机视觉研究的启发，本文采用了一种将HR场中空间频率分离的新方法，试图进一步优化SR GAN。该方法称为频率分离，但导致生成的HR场真实感下降。然而，频率分离确实展示了空间结构如何受用于优化SR模型的指标影响，从而促使开发了更有效的部分频率分离方法。